In view of the neural network's initial structure set depends on the workers experience and its adaptive ability is poor, a dynamic neural network structure design method based on Semi-Supervised Learning (SSL) algorithm was proposed. In order to get a more perfect performance of the initial network structure, the authors trained neural network based on semi-supervised learning method of using both tagged sample and unmarked sample, and judged the impact of the hidden layer neurons on the network output by using Global Sensitivity Analysis method (GSA). The optimal design of dynamic neural network structure was accomplished by cutting or increasing hidden layer neurons based on sensitivity size timely, and the convergence of the dynamic process was investigated. Theoretical analysis and Matlab simulation experiments show that the neural network hidden layer neurons based on Semi-Supervised Learning algorithm will change with training time, and the structure design of the dynamic network is accomplished. The application of hydraulic Automatic Gauge Control (AGC) system, about 160 s later, the system output is becoming stable, and the output error is as small as about 0.03 mm, and compared with Supervised Learning (SL) method and UnSupervised Learning (USL) method, the output error reduces by 0.03 mm and 0.02 mm respectively, which indicate that dynamic network based on SSL algorithm effectively improve the precision of the system output in actual applications.